Liangliang Hao

郝亮亮

Postdoctoral Research Associate

Rm 369, Rhodes Hall,
School of Electrical and Computer Engineering,
Cornell University,
Ithaca, NY, U.S.

Email: LH682@cornell.edu


Biography

I am currently a postdoctoral research associate at School of Electrical and Computer Engineering, Cornell University, supervised by Prof. Lang Tong. My research focuses on the optimization and control for smart grids and smart buildings under uncertainty. I am interested in sequential decision-making problems with stochastic dynamic programming, deep reinforcement learning, and their applications in power systems control and optimization, job scheduling, electrical vehicle (EV) charging, and heating, ventilation, and air conditioning (HVAC) control.

I received B.Eng. degree from School of Electrical Engineering and Automation, Wuhan University in 2016. From 2016 to 2018, I was a research assistant at the Center for Grid Power Electronics and Singapore University of Technology and Design, respectively. I earned my doctoral degree from Department of Mechanical and Automation Engineering, Chinese University of Hong Kong in 2022, supervised by Prof. Yunjian Xu.

Publications [ORCID]

Summary: Conference Paper (1), IEEE Journals (4).

  • Asymptotically Optimal Lagrangian Priority Policy for Deadline Scheduling with Processing Rate Limits
    Liangliang Hao, Yunjian Xu, Lang Tong.
    IEEE Transactions on Automatic Control (IEEE TAC), Full Paper, vol. 67, no. 1, pp. 236-250, Jan., 2022.
    [paper]

  • Laxity Differentiated Pricing and Deadline Differentiated Threshold Scheduling for a Public Electric Vehicle Charging Station
    Liangliang Hao, Jiangliang Jin, Yunjian Xu.
    IEEE Transactions on Industrial Informatics (IEEE TII), vol. 18, no. 9, pp. 6192-6202, Sep., 2022.
    [paper]

  • Semi-supervised Learning based Occupancy Estimation for Real-time Energy Management Using Ambient Data
    Liagliang Hao, Yunjian Xu.
    IEEE Internet Things Journal (IEEE IoT), DOI: 10.1109/JIOT.2023.3280361, May, 2023.
    [paper]

  • Joint Scheduling of Deferrable Demand and Storage with Random Supply and Processing Rate Limits
    Jiangliang Jin, Liagliang Hao, Yunjian Xu, Junjie Wu, Qing-Shan Jia.
    IEEE Transactions on Automatic Control (IEEE TAC), vol. 66, no. 11, pp. 5506-5513, Nov. 2021.
    [paper]

  • Index Policies for Stochastic Deadline Scheduling with Time-varying Processing Rate Limits
    Liangliang Hao, Yunjian Xu.
    2020 American Control Conference, (ACC), Denver, CO, USA, Jul., pp. 204-210, 2020.
    [paper]

Projects

  • Field-Focused Load-Leveled Dynamic Wireless Charging System for Electric Vehicles, funded by U.S. Department of Energy (DOE), 2022 - now

    Researcher
    ◇ Collected and analyze traffic data and EV charging demand in cities and on highways.
    ◇ Developed system-level dynamic wireless charging model for EV charging while driving.
    ◇ Designed and implemented real-time charging optimization algorithms under traffic uncertainty.
    ◇ Wrote reports, set deadlines and milestones, and reported to project managers from DOE.

  • Data-driven and Deep Learning based Smart HVAC Control System for Energy Saving and Thermal Comfort Enhancement, funded by Hong Kong Research Innovation and Technology Commission, 2021 - 2022

    Researcher
    ◇ Learned user habits and thermal dynamics controlled by HVAC systems.
    ◇ Developed data-driven set-point control algorithms for thermostats adaptive to user preferences.
    ◇ Implemented and evaluated control algorithms in fields tests with 10% energy saving.
    ◇ Applied a US patent, being implemented in residential buildings with industrial partners.

  • Action Space Dimension Reduction based on Optimal Control Strategy: Theoretical Research and Application of Reinforcement Learning Method, funded by National Natural Science Foundation of China, 2020 - 2021

    Researcher
    ◇ Proposed optimal charging threshold structures for EVs with differentiated deadlines.
    ◇ Proposed action dimensionality reduction method with advantageous computational efficiency.
    ◇ Developed learning-based EV charging control algorithms with reduced action space.
    ◇ Implemented and evaluated control algorithms in distributed grids via Matpower.

  • Stochastic Deadline Scheduling for Large-scale Electric Vehicle Charging with Renewable Generation and Energy Storage, funded by Hong Kong Research Grants Council, 2018 - 2020

    Researcher
    ◇ Proposed priorities rules for large-scale EV charging scheduling under random renewable generation.
    ◇ Proposed random tie-breaking mechanism for EVs with same priority indices.
    ◇ Mathematically proved the asymptotic optimality of the priority rules for charging profit maximization.

Teaching

  • Teaching Assistant at CUHK

    Fundamentals of Electric Circuits, Fall 2019 and Fall 2020.
    Energy distribution, Spring 2020.
    Energy and Environmental Economics and Management, Spring 2019 and Spring 2021.
    Energy Utilization and Human Behaviour, Fall 2018.

Skills

    Python, MATLAB, Gurobi, Mosek, CVXPY, TensorFlow, PyTorch, Pandas, Scikit-learn, MatPower, Latex.